Businesses are getting more event-driven and adaptive in nature. They are exposed to large amounts of data every day. For Sense and Respond and Business Process Monitoring (BPM), this data needs to be transformed and stored in a database for analysis purposes. Once stored, for example, in a data warehouse businesses use the stored data for analyzing business activities and performing decision making tasks. Traditional data warehouse schemas are designed, in general, independent from the business process and source data. Another type of data warehousing is adaptive data warehousing, as is described in the co-pending U.S. patent application Ser. No. 10/994,232 filed Nov. 23, 2004, entitled “Adaptive Data Warehouse Meta Model”, which is commonly owned by International Business Machines and is hereby incorporated by reference in its entirety.
Businesses typically use a business operations models (“BOM”) and observation models (“OM”) when modeling particular aspects of the business. BOMs generally comprise several packages that include constructs to model a particular aspect of business operations (e.g. processes, resources, information structure, and the like). An OM covers business performance management, which comprises business performance monitoring (observation) and control. OMs are typically constructed top-down starting from the business metrics or key performance indicators (“KPI”) that are to be observed. OMs can also be constructed from business situations that are to be monitored and the metrics needed for defining the business situations.
Monitoring contexts for processing specific events can be designed using OMs. For each relevant incoming event, a monitoring context will typically compute one or more metrics. These metrics are stored in a data warehouse for subsequent analysis. In the context of the data warehouse, some of these metrics are treated as dimensions (e.g. Customer, Time, Location, and the like) and the others as measures (e.g. Revenue, Cost, Profit, and the like). As part of the model-driven approach to design, a database schema of the data warehouse is generated from the OM.
One problem with current data warehouse models is in the way updated OMs are handled. For example, when a new version of an exiting OM is created, the associated data warehouse schema is re-generated. This requires the migration of the already collected data to the new data warehouse schema associated with the new OM. The migration of data causes unnecessary downtime of the data warehouse and disruption of existing data.
Therefore a need exists to overcome the problems with the prior art as discussed above.